Machine learning applied to estimating broadband source signature characteristics in a shallow ocean environment

Abstract

Probabilistic machine and deep learning methods are critical elements in the area of automation and artificial intelligence. The regression task considered here is to use supervised machine learning to predict a frequency dependent acoustic source level from received broadband signals that have propagated through a shallow ocean waveguide possessing random features. The details are intimately linked to a previously introduced sequential maximum entropy—Bayesian approach employed to generate marginal probability distributions for both environmental and source parameter values. To meet the requirement to have both low training and generalization errors, several regularization and non-linear optimization methods are considered to enhance performance. This includes convolutional networks. Optimization includes not only the issue of sampling but also finding an effective model capacity, which is one of the most significant challenges to successful machine learning applications. The methodology is applied to measured broadband data collected in about 75 m of water about 60 miles south of Cape Cod Massachusetts in an area called the mud pond. Both the training samples and the samples not used in training have known source levels with which to measure both the training error and the generalization error and thus quantify performance.